{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-09T11:52:58.244127Z",
     "start_time": "2025-06-09T11:52:57.387642Z"
    }
   },
   "source": [
    "# ==================== 1. 依赖库导入 ====================\n",
    "import os\n",
    "import re\n",
    "import jieba\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from tqdm import tqdm\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import Perceptron, LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, classification_report\n"
   ],
   "outputs": [],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-09T07:51:44.109852Z",
     "start_time": "2025-06-09T07:49:31.705280Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# ==================== 2. 数据预处理 ====================\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "\n",
    "def preprocess_text(text):\n",
    "    # 清洗特殊字符\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
    "    # 文本标准化\n",
    "    text = text.lower()\n",
    "    words = word_tokenize(text)\n",
    "    # 停用词过滤\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    words = [word for word in words if word not in stop_words]\n",
    "    # 词形还原\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    words = [lemmatizer.lemmatize(word) for word in words]\n",
    "    # 过滤短词\n",
    "    return \" \".join([word for word in words if len(word) > 2])\n"
   ],
   "id": "28cf27f7c343e69e",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading stopwords: Remote end closed connection\n",
      "[nltk_data]     without response\n",
      "[nltk_data] Error loading wordnet: Remote end closed connection\n",
      "[nltk_data]     without response\n",
      "[nltk_data] Downloading package punkt to D:\\python\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-09T11:47:44.638812Z",
     "start_time": "2025-06-09T11:47:44.624217Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#------------------数据处理函数--------------------\n",
    "def load_class_labels(class_path):\n",
    "    label_map = {}\n",
    "    if os.path.exists(class_path):\n",
    "        with open(class_path, 'r', encoding='utf-8') as f:\n",
    "            for idx, line in enumerate(f):\n",
    "                label_map[idx] = line.strip()\n",
    "    return label_map or {idx: f\"类别{idx}\" for idx in sorted(set(label_map.keys()))}\n",
    "\n",
    "\n",
    "def load_text_dataset(file_path):\n",
    "    texts, labels = [], []\n",
    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
    "        for line in tqdm(f, desc=f\"加载 {file_path}\"):\n",
    "            line = line.strip()\n",
    "            if not line:\n",
    "                continue\n",
    "            parts = line.rsplit('\\t', 1)\n",
    "            if len(parts) == 2:\n",
    "                texts.append(parts[0])\n",
    "                labels.append(int(parts[1]))\n",
    "    return texts, labels\n",
    "\n",
    "\n",
    "def preprocess_texts(texts, stopwords):\n",
    "    processed = []\n",
    "    for text in tqdm(texts, desc=\"文本预处理\"):\n",
    "        words = jieba.cut(text, HMM=True)\n",
    "        filtered = [word for word in words if word not in stopwords and len(word) >= 2]\n",
    "        processed.append(' '.join(filtered))\n",
    "    return processed"
   ],
   "id": "547918e747e3696",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-09T11:51:41.003142Z",
     "start_time": "2025-06-09T11:51:40.997041Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_imdb_data(data_path):\n",
    "    train_texts, train_labels = [], []\n",
    "    test_texts, test_labels = [], []\n",
    "\n",
    "    for label in ['pos', 'neg']:\n",
    "        for split in ['train', 'test']:\n",
    "            path = os.path.join(data_path, split, label)\n",
    "            for file_name in os.listdir(path):\n",
    "                if file_name.endswith('.txt'):\n",
    "                    with open(os.path.join(path, file_name), 'r', encoding='utf-8') as file:\n",
    "                        text = file.read()\n",
    "                        if split == 'train':\n",
    "                            train_texts.append(text)\n",
    "                            train_labels.append(1 if label == 'pos' else 0)\n",
    "                        else:\n",
    "                            test_texts.append(text)\n",
    "                            test_labels.append(1 if label == 'pos' else 0)\n",
    "    return train_texts, train_labels, test_texts, test_labels"
   ],
   "id": "4bdb970e7c4d9455",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-09T11:50:53.501311Z",
     "start_time": "2025-06-09T11:47:46.183100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# ------------------------------ 模型训练函数 ------------------------------\n",
    "def run_naive_bayes(data, stopwords, label_map):\n",
    "    train_texts, train_labels, test_texts, test_labels = data\n",
    "    classes = list(label_map.values())\n",
    "\n",
    "    # 特征提取（TF-IDF）\n",
    "    vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n",
    "    X_train = vectorizer.fit_transform(preprocess_texts(train_texts, stopwords))\n",
    "    X_test = vectorizer.transform(preprocess_texts(test_texts, stopwords))\n",
    "\n",
    "    # 初始化朴素贝叶斯模型\n",
    "    model = MultinomialNB()\n",
    "\n",
    "    # 训练模型\n",
    "    start_time = datetime.now()\n",
    "    model.fit(X_train, train_labels)\n",
    "    train_time = (datetime.now() - start_time).total_seconds()\n",
    "\n",
    "    # 预测\n",
    "    train_pred = model.predict(X_train)\n",
    "    test_pred = model.predict(X_test)\n",
    "    train_acc = accuracy_score(train_labels, train_pred)\n",
    "    test_acc = accuracy_score(test_labels, test_pred)\n",
    "\n",
    "    # 计算混淆矩阵\n",
    "    conf_matrix = confusion_matrix(test_labels, test_pred)\n",
    "\n",
    "    # 输出结果\n",
    "    print(f\"\\n---------------- 朴素贝叶斯模型 ----------------\")\n",
    "    print(f\"训练集准确率: {train_acc:.4f}\")\n",
    "    print(f\"测试集准确率: {test_acc:.4f}\")\n",
    "    print(f\"训练时间: {train_time:.2f} 秒\")\n",
    "    print(\"分类报告:\\n\", classification_report(test_labels, test_pred, target_names=classes,digits=4))\n",
    "\n",
    "    return {\n",
    "        \"model\": \"朴素贝叶斯\",\n",
    "        \"test_accuracy\": test_acc,\n",
    "        \"train_time\": train_time,\n",
    "        \"conf_matrix\": conf_matrix,\n",
    "        \"classes\": classes\n",
    "    }\n",
    "\n",
    "\n",
    "# ------------------------------ 绘制混淆矩阵函数 ------------------------------\n",
    "def plot_confusion_matrix(conf_matrix, classes, dataset_name):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.title(f'{dataset_name} - 朴素贝叶斯模型混淆矩阵')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 绘制柱状图函数（对比不同数据集的准确率和训练时间） ------------------------------\n",
    "def plot_bar_charts(results, dataset_names):\n",
    "    # 提取准确率和训练时间数据\n",
    "    accuracies = [result['test_accuracy'] for result in results]\n",
    "    train_times = [result['train_time'] for result in results]\n",
    "\n",
    "    x = np.arange(len(dataset_names))  # 数据集数量\n",
    "    width = 0.35\n",
    "\n",
    "    # 绘制准确率对比柱状图\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.bar(x, accuracies, width, label='测试准确率', color='skyblue')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('准确率')\n",
    "    plt.title('不同数据集上朴素贝叶斯模型测试准确率对比')\n",
    "    for i, acc in enumerate(accuracies):\n",
    "        plt.text(i, acc + 0.02, f'{acc:.4f}', ha='center')\n",
    "\n",
    "    # 绘制训练时间对比柱状图\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.bar(x, train_times, width, label='训练时间', color='orange')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('时间（秒）')\n",
    "    plt.title('不同数据集上朴素贝叶斯模型训练时间对比')\n",
    "    max_time = max(train_times) if train_times else 0.1  # 处理空数据\n",
    "    plt.ylim(0, max_time + max_time * 0.5)  # 扩展 50% 空间\n",
    "    \n",
    "    for i, time in enumerate(train_times):\n",
    "        offset = max_time * 0.1  # 用数据最大值的 10% 作为偏移\n",
    "        plt.text(i, time + offset, f'{time:.2f}s', ha='center')\n",
    "    #for i, time in enumerate(train_times):\n",
    "        #plt.text(i, time + 0.1, f'{time:.2f}s', ha='center')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 主函数（调用模型、绘图等） ------------------------------\n",
    "def main():\n",
    "    print(f\"[{datetime.now()}] 文本分类实验开始...\")\n",
    "\n",
    "    # 新闻标题数据集\n",
    "    THUCNEWS_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\"\n",
    "    THUCNEWS_CLASS_PATH = os.path.join(THUCNEWS_DATA_DIR, \"class.txt\")\n",
    "    stopwords = {\n",
    "        '的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一',\n",
    "        '个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有'\n",
    "    }\n",
    "\n",
    "    # 加载新闻标题数据集\n",
    "    thucnews_train_texts, thucnews_train_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"train.txt\"))\n",
    "    thucnews_test_texts, thucnews_test_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"test.txt\"))\n",
    "    thucnews_label_map = load_class_labels(THUCNEWS_CLASS_PATH)\n",
    "    thucnews_data = (thucnews_train_texts, thucnews_train_labels, thucnews_test_texts, thucnews_test_labels)\n",
    "\n",
    "    # 输出新闻标题数据集信息\n",
    "    print(f\"新闻标题训练集样本数：{len(thucnews_train_texts)}\")\n",
    "    print(f\"新闻标题测试集样本数：{len(thucnews_test_texts)}\")\n",
    "\n",
    "    # 运行朴素贝叶斯模型 - 新闻标题数据集\n",
    "    thucnews_result = run_naive_bayes(thucnews_data, stopwords, thucnews_label_map)\n",
    "    # 绘制新闻标题数据集的混淆矩阵\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "    plot_confusion_matrix(thucnews_result['conf_matrix'], thucnews_result['classes'], '新闻标题数据集')\n",
    "\n",
    "    # 电影评论数据集\n",
    "    IMDB_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\aclImdb\"\n",
    "\n",
    "    # 加载电影评论数据集\n",
    "    imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels = load_imdb_data(IMDB_DATA_DIR)\n",
    "    imdb_label_map = {0: 'neg', 1: 'pos'}\n",
    "    imdb_data = (imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels)\n",
    "\n",
    "    # 输出电影评论数据集信息\n",
    "    print(f\"电影评论训练集样本数：{len(imdb_train_texts)}\")\n",
    "    print(f\"电影评论测试集样本数：{len(imdb_test_texts)}\")\n",
    "\n",
    "    # 运行朴素贝叶斯模型 - 电影评论数据集\n",
    "    imdb_result = run_naive_bayes(imdb_data, stopwords, imdb_label_map)\n",
    "    # 绘制电影评论数据集的混淆矩阵\n",
    "    plot_confusion_matrix(imdb_result['conf_matrix'], imdb_result['classes'], '电影评论数据集')\n",
    "\n",
    "    # 整理结果，用于绘制柱状图\n",
    "    results = [thucnews_result, imdb_result]\n",
    "    dataset_names = ['新闻标题数据集', '电影评论数据集']\n",
    "\n",
    "    # 绘制准确率和训练时间对比柱状图\n",
    "    plot_bar_charts(results, dataset_names)\n",
    "\n",
    "    # 打印结果汇总\n",
    "    print(\"\\n==================== 结果汇总 ====================\")\n",
    "    for i in range(len(results)):\n",
    "        print(f\"数据集：{dataset_names[i]} | 模型：{results[i]['model']} | 测试准确率：{results[i]['test_accuracy']:.4f} | 训练时间：{results[i]['train_time']:.2f}s\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ],
   "id": "156e91a41d91713e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-06-09 19:47:46.199225] 文本分类实验开始...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\train.txt: 180000it [00:00, 1257871.91it/s]\n",
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\test.txt: 10000it [00:00, 632539.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新闻标题训练集样本数：180000\n",
      "新闻标题测试集样本数：10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 180000/180000 [00:10<00:00, 17148.05it/s]\n",
      "文本预处理: 100%|██████████| 10000/10000 [00:00<00:00, 17161.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- 朴素贝叶斯模型 ----------------\n",
      "训练集准确率: 0.8715\n",
      "测试集准确率: 0.8637\n",
      "训练时间: 0.05 秒\n",
      "分类报告:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      finance     0.8319    0.8710    0.8510      1000\n",
      "       realty     0.9354    0.8400    0.8851      1000\n",
      "       stocks     0.8217    0.8110    0.8163      1000\n",
      "    education     0.9049    0.9330    0.9188      1000\n",
      "      science     0.8373    0.8080    0.8224      1000\n",
      "      society     0.8324    0.8840    0.8574      1000\n",
      "     politics     0.8242    0.8530    0.8383      1000\n",
      "       sports     0.9149    0.9240    0.9194      1000\n",
      "         game     0.9069    0.8570    0.8812      1000\n",
      "entertainment     0.8392    0.8560    0.8475      1000\n",
      "\n",
      "     accuracy                         0.8637     10000\n",
      "    macro avg     0.8649    0.8637    0.8638     10000\n",
      " weighted avg     0.8649    0.8637    0.8638     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "电影评论训练集样本数：25000\n",
      "电影评论测试集样本数：25000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 25000/25000 [01:19<00:00, 314.68it/s]\n",
      "文本预处理: 100%|██████████| 25000/25000 [00:59<00:00, 419.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- 朴素贝叶斯模型 ----------------\n",
      "训练集准确率: 0.8806\n",
      "测试集准确率: 0.8621\n",
      "训练时间: 0.02 秒\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         neg     0.8635    0.8601    0.8618     12500\n",
      "         pos     0.8606    0.8641    0.8624     12500\n",
      "\n",
      "    accuracy                         0.8621     25000\n",
      "   macro avg     0.8621    0.8621    0.8621     25000\n",
      "weighted avg     0.8621    0.8621    0.8621     25000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================== 结果汇总 ====================\n",
      "数据集：新闻标题数据集 | 模型：朴素贝叶斯 | 测试准确率：0.8637 | 训练时间：0.05s\n",
      "数据集：电影评论数据集 | 模型：朴素贝叶斯 | 测试准确率：0.8621 | 训练时间：0.02s\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-09T11:55:55.673096Z",
     "start_time": "2025-06-09T11:53:26.765108Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import jieba\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "import seaborn as sns  # 用于绘制更美观的混淆矩阵\n",
    "# ------------------------------ 模型训练函数 ------------------------------\n",
    "def run_decision_tree(data, stopwords, label_map):\n",
    "    train_texts, train_labels, test_texts, test_labels = data\n",
    "    classes = list(label_map.values())\n",
    "\n",
    "    # 特征提取（TF-IDF）\n",
    "    vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n",
    "    X_train = vectorizer.fit_transform(preprocess_texts(train_texts, stopwords))\n",
    "    X_test = vectorizer.transform(preprocess_texts(test_texts, stopwords))\n",
    "\n",
    "    # 初始化决策树模型（限制树深防止过拟合）\n",
    "    model = DecisionTreeClassifier(max_depth=10, random_state=42)\n",
    "\n",
    "    # 训练模型\n",
    "    start_time = datetime.now()\n",
    "    model.fit(X_train, train_labels)\n",
    "    train_time = (datetime.now() - start_time).total_seconds()\n",
    "\n",
    "    # 预测\n",
    "    train_pred = model.predict(X_train)\n",
    "    test_pred = model.predict(X_test)\n",
    "    train_acc = accuracy_score(train_labels, train_pred)\n",
    "    test_acc = accuracy_score(test_labels, test_pred)\n",
    "\n",
    "    # 计算混淆矩阵\n",
    "    conf_matrix = confusion_matrix(test_labels, test_pred)\n",
    "\n",
    "    # 输出结果\n",
    "    print(f\"\\n---------------- 决策树模型 ----------------\")\n",
    "    print(f\"训练集准确率: {train_acc:.4f}\")\n",
    "    print(f\"测试集准确率: {test_acc:.4f}\")\n",
    "    print(f\"训练时间: {train_time:.2f} 秒\")\n",
    "    print(\"分类报告:\\n\", classification_report(test_labels, test_pred, target_names=classes,digits=4))\n",
    "\n",
    "    return {\n",
    "        \"model\": \"决策树\",\n",
    "        \"test_accuracy\": test_acc,\n",
    "        \"train_time\": train_time,\n",
    "        \"conf_matrix\": conf_matrix,\n",
    "        \"classes\": classes\n",
    "    }\n",
    "\n",
    "\n",
    "# ------------------------------ 绘制混淆矩阵函数 ------------------------------\n",
    "def plot_confusion_matrix(conf_matrix, classes, dataset_name):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.title(f'{dataset_name} - 决策树模型混淆矩阵')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 绘制柱状图函数（对比不同数据集的准确率和训练时间） ------------------------------\n",
    "def plot_bar_charts(results, dataset_names):\n",
    "    # 提取准确率和训练时间数据\n",
    "    accuracies = [result['test_accuracy'] for result in results]\n",
    "    train_times = [result['train_time'] for result in results]\n",
    "\n",
    "    x = np.arange(len(dataset_names))  # 数据集数量\n",
    "    width = 0.35\n",
    "\n",
    "    # 绘制准确率对比柱状图\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.bar(x, accuracies, width, label='测试准确率', color='skyblue')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('准确率')\n",
    "    plt.title('不同数据集上决策树模型测试准确率对比')\n",
    "    for i, acc in enumerate(accuracies):\n",
    "        plt.text(i, acc + 0.02, f'{acc:.4f}', ha='center')\n",
    "\n",
    "    # 绘制训练时间对比柱状图\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.bar(x, train_times, width, label='训练时间', color='orange')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('时间（秒）')\n",
    "    plt.title('不同数据集上决策树模型训练时间对比')\n",
    "    for i, time in enumerate(train_times):\n",
    "        plt.text(i, time + 0.1, f'{time:.2f}s', ha='center')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 主函数（调用模型、绘图等） ------------------------------\n",
    "def main():\n",
    "    print(f\"[{datetime.now()}] 文本分类实验开始...\")\n",
    "\n",
    "    # 新闻标题数据集\n",
    "    THUCNEWS_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\"\n",
    "    THUCNEWS_CLASS_PATH = os.path.join(THUCNEWS_DATA_DIR, \"class.txt\")\n",
    "    stopwords = {\n",
    "        '的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一',\n",
    "        '个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有'\n",
    "    }\n",
    "\n",
    "    # 加载新闻标题数据集\n",
    "    thucnews_train_texts, thucnews_train_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"train.txt\"))\n",
    "    thucnews_test_texts, thucnews_test_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"test.txt\"))\n",
    "    thucnews_label_map = load_class_labels(THUCNEWS_CLASS_PATH)\n",
    "    thucnews_data = (thucnews_train_texts, thucnews_train_labels, thucnews_test_texts, thucnews_test_labels)\n",
    "\n",
    "    # 输出新闻标题数据集信息\n",
    "    print(f\"新闻标题训练集样本数：{len(thucnews_train_texts)}\")\n",
    "    print(f\"新闻标题测试集样本数：{len(thucnews_test_texts)}\")\n",
    "\n",
    "    # 运行决策树模型 - 新闻标题数据集\n",
    "    thucnews_result = run_decision_tree(thucnews_data, stopwords, thucnews_label_map)\n",
    "    # 绘制新闻标题数据集的混淆矩阵\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "    plot_confusion_matrix(thucnews_result['conf_matrix'], thucnews_result['classes'], '新闻标题数据集')\n",
    "\n",
    "    # 电影评论数据集\n",
    "    IMDB_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\aclImdb\"\n",
    "\n",
    "    # 加载电影评论数据集\n",
    "    imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels = load_imdb_data(IMDB_DATA_DIR)\n",
    "    imdb_label_map = {0: 'neg', 1: 'pos'}\n",
    "    imdb_data = (imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels)\n",
    "\n",
    "    # 输出电影评论数据集信息\n",
    "    print(f\"电影评论训练集样本数：{len(imdb_train_texts)}\")\n",
    "    print(f\"电影评论测试集样本数：{len(imdb_test_texts)}\")\n",
    "\n",
    "    # 运行决策树模型 - 电影评论数据集\n",
    "    imdb_result = run_decision_tree(imdb_data, stopwords, imdb_label_map)\n",
    "    # 绘制电影评论数据集的混淆矩阵\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "    plot_confusion_matrix(imdb_result['conf_matrix'], imdb_result['classes'], '电影评论数据集')\n",
    "\n",
    "    # 整理结果，用于绘制柱状图\n",
    "    results = [thucnews_result, imdb_result]\n",
    "    dataset_names = ['新闻标题数据集', '电影评论数据集']\n",
    "\n",
    "    # 绘制准确率和训练时间对比柱状图\n",
    "    plot_bar_charts(results, dataset_names)\n",
    "\n",
    "    # 打印结果汇总\n",
    "    print(\"\\n==================== 结果汇总 ====================\")\n",
    "    for i in range(len(results)):\n",
    "        print(f\"数据集：{dataset_names[i]} | 模型：{results[i]['model']} | 测试准确率：{results[i]['test_accuracy']:.4f} | 训练时间：{results[i]['train_time']:.2f}s\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ],
   "id": "2ec8df34c05b3092",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-06-09 19:53:26.777750] 文本分类实验开始...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\train.txt: 180000it [00:00, 1402368.17it/s]\n",
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\test.txt: 10000it [00:00, 600644.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新闻标题训练集样本数：180000\n",
      "新闻标题测试集样本数：10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 180000/180000 [00:10<00:00, 17574.59it/s]\n",
      "文本预处理: 100%|██████████| 10000/10000 [00:01<00:00, 8369.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- 决策树模型 ----------------\n",
      "训练集准确率: 0.2293\n",
      "测试集准确率: 0.2288\n",
      "训练时间: 1.50 秒\n",
      "分类报告:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      finance     0.9536    0.3080    0.4656      1000\n",
      "       realty     0.9809    0.3080    0.4688      1000\n",
      "       stocks     0.5000    0.0020    0.0040      1000\n",
      "    education     0.9934    0.3000    0.4608      1000\n",
      "      science     1.0000    0.0020    0.0040      1000\n",
      "      society     0.9527    0.1610    0.2754      1000\n",
      "     politics     0.0000    0.0000    0.0000      1000\n",
      "       sports     0.1151    0.9960    0.2063      1000\n",
      "         game     0.9336    0.2110    0.3442      1000\n",
      "entertainment     0.0000    0.0000    0.0000      1000\n",
      "\n",
      "     accuracy                         0.2288     10000\n",
      "    macro avg     0.6429    0.2288    0.2229     10000\n",
      " weighted avg     0.6429    0.2288    0.2229     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "电影评论训练集样本数：25000\n",
      "电影评论测试集样本数：25000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 25000/25000 [00:50<00:00, 495.79it/s]\n",
      "文本预处理: 100%|██████████| 25000/25000 [00:48<00:00, 515.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- 决策树模型 ----------------\n",
      "训练集准确率: 0.7676\n",
      "测试集准确率: 0.7224\n",
      "训练时间: 7.77 秒\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         neg     0.7408    0.6842    0.7114     12500\n",
      "         pos     0.7066    0.7606    0.7326     12500\n",
      "\n",
      "    accuracy                         0.7224     25000\n",
      "   macro avg     0.7237    0.7224    0.7220     25000\n",
      "weighted avg     0.7237    0.7224    0.7220     25000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================== 结果汇总 ====================\n",
      "数据集：新闻标题数据集 | 模型：决策树 | 测试准确率：0.2288 | 训练时间：1.50s\n",
      "数据集：电影评论数据集 | 模型：决策树 | 测试准确率：0.7224 | 训练时间：7.77s\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "3fd8420b4320d310"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
